(a) Field of the Invention
The present invention relates to a method of definition of a navigation system. It applies notably to the field of platform navigation, and more particularly to navigation systems operating notably in accordance with Terrain-Aided-Navigation methods, Terrain-Aided-Navigation being designated by the initials TAN.
(b) Description of the Related Art
Terrain-Aided-Navigation or TAN, constitutes a particular means of navigation that can be applied to a great diversity of vehicles, for example aircraft, submarines, self-guided missiles, etc.
There exist three main known means aimed at meeting the navigation requirements of platforms. The first main known means comprises inertial navigation techniques. The second main known means comprises radionavigation techniques. The third main known means comprises Terrain-Aided-Navigation techniques.
Inertial navigation consists in utilizing information provided by inertial platforms. The operation of an inertial platform is based on the Einstein-Galileo relativity principle, postulating that it is possible, without the aid of signals exterior to the platform, to measure on the one hand the speed of rotation of the platform with respect to an inertial frame of reference, for example defined by a geocentric reference frame associated with fixed stars, and on the other hand the specific force applied to the platform: typically its acceleration in the inertial frame of reference, decreased by the acceleration due to gravity. Typically, an inertial navigation system, commonly designated by the initials INS, is a device making it possible to measure these two quantities by means of sensors such as gyrometers and accelerometers, commonly three of each type, disposed along three orthogonal axes, the set of these sensors forming an inertial measurement unit commonly designated by the initials “IMU”. Temporal integration of the acceleration data, and projection into the navigation reference frame on the basis of the speed of rotation data, allow the determination of the position and speed of the platform relative to the Earth, knowing an initial state of these data. However, a drawback related to temporal integration is that the error associated with the data thus determined is an increasing function of time. This error increases more than linearly, typically in an exponential manner, the variation of the error commonly being called the drift of the inertial platform. It is thus necessary, for applications requiring precision navigation, to merge the inertial measurements with other measurements of position and/or speed and/or attitude of the platform that are provided by complementary sensors, such as baroaltimeters, odometers, Pitot probes, etc., with the aim of decreasing the drift of the inertial platform. Such sensors provide information on the kinematic state of the platform without requiring any access to exterior signals or onboard maps, and are commonly called low-level sensors.
Radionavigation consists in utilizing the signals arising from beacons emitting radioelectric signals, so as to derive information regarding the positioning of the platform in relation to these beacons. A widely used radionavigation technique is the satellite-based geo-positioning technique, commonly designated according to the initials GNSS corresponding to the conventional terminology “Global Navigation Satellite System”, and one of the representatives of which is the GPS technique, corresponding to the conventional terminology “Global Positioning System”. One of the drawbacks specific to radionavigation techniques is related to the fact that reception of the signals originating from the beacons is not guaranteed everywhere at every moment, and may notably be affected by the geophysical environment of the platform, as well as by the surrounding electromagnetic noise, jamming techniques being able notably to jeopardize the operation of a radionavigation device. Furthermore, since the emitting beacons are maintained by operators, the integrity of the radionavigation data arising therefrom is greatly dependent on their good will. Radionavigation, and notably satellite-based geo-positioning and inertial navigation, are for example complementary navigation techniques, and a hybridization of the two techniques may turn out to be very efficient in practice. Inertial navigation indeed constitutes a very good local estimator of long-term drifting positioning, with satellite-based geo-positioning being rather unreliable over a short duration because of the aforementioned drawbacks, but not exhibiting any drift. However, in the most critical applications, and notably for military applications, it is essential to resort to other sources of information regarding position and/or speed and/or attitude of the platform so as to ensure hybridization with an inertial navigation technique. It is notably desirable that these alternative sources allow measurements of position and/or speed and/or of attitude of the platform which are autonomous, not prone to jamming, and discrete.
Terrain-Aided-Navigation or TAN consists in utilizing geophysical measurements of data delivered by an appropriate sensor, with reference data specific to a terrain of coverage of the navigation. The sensors are thus used jointly with a reference map of the terrain, also called an onboard map. These sensors allow the reading of a characteristic datum about the terrain, and Terrain-Aided-Navigation consists in comparing these values with the data of the onboard map, the onboard map being an a priori survey, obtained by appropriate means, of the values of these data over the navigation zone considered and hereinafter called data production pathway. Terrain-Aided-Navigation is particularly adapted for hybridization with an inertial navigation technique, and makes it possible to alleviate the inadequacies of radionavigation. Of course, for optimal performance, it is possible to resort to a navigation system allowing hybridization of the three aforementioned navigation techniques.
Generally, any navigation system involving terrain correlation thus comprises a plurality of onboard sensors included in the inertial platform, as well as the terrain sensor, an onboard map representing the best possible knowledge about the reality of the geophysical data that the onboard sensor must measure, and a navigation filter. The navigation filter makes it possible to arbitrate in real time, between the information provided by the inertial platform and that provided by the comparison between the measurements provided by the terrain sensor and the onboard map. The arbitration is carried out by the filter as a function of its a priori knowledge of the errors in the measurements provided. This knowledge is grouped together in error models. The error models relate to the inertial platform, the errors of the inertial platform being greater or smaller according to the quality of the equipment; the error models also relate to the terrain sensor, and also the onboard map, the errors of the latter being greater or smaller according to the quality of the data production pathway. The equipment error models originate from the information provided by the manufacturers, and/or from measurements performed via specific studies. The error models of the onboard maps are provided by the data producers.
An essential aspect of navigation is the stochastic nature of the phenomena considered. Indeed, sensors produce errors in accordance with stochastic models, and the knowledge of the geophysical data being rather uncertain, solving the problem of navigation by filtering renders the navigation performance intrinsically stochastic. Thus, the filter used in a navigation system may be considered to be an estimator of a stochastic process, that is to say to be the device which at any instant gives the dynamic state of the platform modeled as a random variable.
A first exemplary navigation system involving terrain correlation is based on the altimetric navigation technique. This technique consists in navigating an aerial platform with the aid of an inertial platform, of a terrain sensor like a radioaltimeter or a multi-beam laser scanner, measuring the distance from the platform to the terrain in one or more given direction(s), and of an onboard map of Digital Terrain Model or DTM type, charting the altitudes of points of the ground on a geolocalized regular grid.
A second exemplary navigation system involving terrain correlation is based on the bathymetric navigation technique. This technique consists in navigating a sea platform or underwater platform with the aid of an inertial platform, of a terrain sensor of mono-beam or multi-beam bathymetric sounder type, measuring the distance from the platform to the bottom of the sea in one or more given direction(s), and of an onboard map of bathymetric map type, charting the altitudes of points of the sea bed on a geolocalized regular grid.
A third exemplary navigation system involving terrain correlation is based on the gravimetric navigation technique. This technique consists in navigating an air, sea or underwater platform with the aid of an inertial platform, of a terrain sensor of gravimeter or accelerometer type, measuring the local gravity field or its anomaly, and of an onboard map of gravimetric anomaly map type, charting the values of the anomaly of the gravity field at points of the globe on a standardized regular grid.
A fourth exemplary navigation system involving terrain correlation is based on the technique of navigation by vision. This technique consists in navigating an aerial platform with the aid of an inertial platform, of a terrain sensor of onboard camera type which delivers images of the landscape overflown at a given frequency in the visible or infrared region, and of two onboard maps, an onboard map of geolocalized Orthoimage type, that is to say an image re-sampled in such a way that the effects of the relief have been deleted, that is to say an image for which the scale is the same at all points, as well as an onboard map of DTM type.
Within the framework of navigation systems involving terrain correlation, designers are notably confronted with a certain number of technical problems stated hereinbelow:                it is necessary to define a navigation system making it possible to achieve a desired navigation quality according to a set of determined performance criteria, for example guaranteeing a mean positioning error of less than a given threshold, doing so at minimal cost;        it is necessary to determine the most faithful possible error models for the inertial platform, the terrain sensor and the onboard map;        it is necessary to define the missions of a platform, notably in terms of benchmark trajectory, during a mission preparation phase, so as to determine an optimal trajectory along which the quality of the signal delivered by the terrain sensor is a maximum, the optimal trajectory having also to be defined as a function of other performance criteria of the mission of the platform and of operational constraints related to the theater of the mission. The mission preparation phase must for example be based on a navigability criterion which is relevant, that is to say representative of the richness of the signal delivered by the terrain sensor;        it is necessary to define an efficient and robust navigation filter capable of taking into consideration at best all the error models relating to the various hardware components of the system, that is to say the error of the inertial platform, of the terrain sensor and of the onboard map.        